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Point Cloud of Cloud Points (PCCP) VAP provides a stereo reconstructed Point Cloud of Cloud Points (PCCP) that correspond to the x,y,z coordinates of cloud features detected from stereo image pairs generated from a multi-view stereo system deployed to collect exclusive set of data on shallow cumulus clouds overhead.
Atmospheric radiative forcing, surface radiation budget, and top of the atmosphere radiance interpretation involves a knowledge of the vertical height structure of overlying cloud and aerosol layers. During the last decade, the U.S. Department of Energy through I the Atmospheric Radiation Measurement (ARM) program has constructed four long- term atmospheric observing sites in strategic climate regimes (north central Oklahoma, In Barrow. Alaska, and Nauru and Manus Islands in the tropical western Pacific). Micro Pulse Lidar (MPL) systems provide continuous, autonomous observation of all significant atmospheric cloud and aerosol at each of the central ARM facilities. Systems are compact and transmitted pulses are eye-safe. Eye-safety is achieved by expanding relatively low-powered outgoing Pulse energy through a shared, coaxial transmit/receive telescope. ARM NIPL system specifications, and specific unit optical designs are discussed. Data normalization and calibration techniques are presented. A multiple cloud boundary detection algorithm is also described. These techniques in tandem represent an operational value added processing package used to produce normalized data products for Cloud and aerosol research and the historical ARM data archive.
This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.
Are domain-aggregated, long-term climate trends in retrieved reflected solar cloud properties detectable despite instrument related and algorithm-related aliasing errors? Science Question: By how much do changes in instrument calibration distort reflected solar cloud property decadal trends? Objective: Quantify how changes in calibration error feed into cloud property changes. Science Question: By how much do reflected solar cloud property retrieval assumptions distort trends in retrieved cloud properties? Objective: Quantify how the plane-parallel assumption in reflected solar satellite cloud retrievals distorts cloud optical thickness decadal trends by determining how and if the large-scale 3D optical thickness bias changes over time. - The three panel procedure below will be performed for each time (month), (zonal) region, set of solar and viewing geometries.
On July 14, 1987 NASA's ER-2 high altitude aircraft flew a mission to measure the structure of stratocumulus clouds off the coast of California. A flight pattern was executed so that the two-dimensional variability of the clouds could be detected. The technique of analysis of the lidar data to measure cloud tops follows. First each signal is searched for its maximum in return strength. This maximum is caused by scattering of the laser light off cloud particles or from the ocean surface. Next the variance of the signal return above the level of maximum backscatter is determined. Cloud top is assigned to a level (above the level of maximum backscatter) where the backscatter exceeds the average variance. This two-step process is necessary because the level of maximum backscatter does not correspond to the cloud top. Ocean surface returns are easily separated from cloud returns in this process, described in detail by Boers, Spinhirne, and Hart (1988). Analysis of the data so far has shown that there were very few breaks in the clouds. Furthermore the layer top was very flat with local oscillations not exceeding 30 m. Such small cloud top variations are still well within the range of detectability, because the precision of this technique of cloud top detection has previously been established to be 13 to 15 m. Data are presently being analyzed to compute cloud top distributions and fractional cloudiness. The aim of this research is to relate the fractional cloudiness to the mean thermodynamic structure of the boundary layer. Researchers plan to compute spectral scales of the cloud top variability in two dimensions to determine the orientation of the clouds with respect to the mean wind. Furthermore the lidar derived cloud top distribution will be used in the computation of the thermodynamic and radiation budget of the boundary layer.
Using spectral imaging data acquired with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) from an ER-2 aircraft at 20 km altitude during various field programs, it was found that narrow channels near the center of the strong 1.38-micrometer water vapor band are very effective in detecting think cirrus clouds. Based on this observation from AVIRIS data, Gao and Kaufman proposed to put a channel centered at 1.375 micrometers with a width of 30 nm on the Moderate Resolution Imaging Spectrometer (MODIS) for remote sensing of cirrus clouds from space. The sensitivity of the 1.375-micrometer MODIS channel to detect thin cirrus clouds during the day time is expected to be one to two orders of magnitude better than the current infrared emission techniques. As a result, much larger fraction of the satellite data is expected to be identified as being covered by cirrus clouds, some of them so thin that their obscuration of the surface is very small. In order to make better studies of surface reflectance properties, thin cirrus effects must be removed from satellite images. Therefore, there is a need to study radiative properties of thin cirrus clouds, so that a strategy for correction or removal of the thin cirrus effects, similar to the correction of atmospheric aerosol effect, can be formed. In this extended abstract, we describe an empirical approach for removing/correcting thin cirrus effects in AVIRIS images using channels near 1.375 microns - one step beyond the detection of cirrus clouds using these channels.
Progress in the discovery and study of interstellar molecules is summarized. The 36 molecular species thus far identified in interstellar space are listed in several groups which include simple hydrides, oxides, and sulfides, various derivatives of ammonia, molecules involving linear carbon chains, cyanides, and molecules related in structure to formaldehyde, alcohols, or ethers. Several free radicals are described, the discovery of molecules in external galaxies is discussed, and possible mechanisms for molecular formation are noted. Methods for examining relative isotopic abundances by measuring molecules in interstellar clouds are outlined, mechanisms for the excitation of interstellar molecules are reviewed, and values are presented for the C-12/C-13 abundance ratio in a number of interstellar clouds. The detection of interstellar masers is discussed along with pumping mechanisms and masing transitions in H2CO, CH, OH, and SiO. The nature of dense interstellar clouds is examined in terms of several simple and complex cloud models, with emphasis on multiple condensation models.
During a routine ER-2 aircraft high-altitude test flight on April 18, 1997, an unusual aerosol cloud was detected at 20 km altitude near the California coast at about 370 degrees N latitude. Not visually observed by the ER-2 pilot, the cloud was characterized bv high concentration of soot and sulfate aerosol in a region over 100 km in horizontal extent indicating that the source of the plume was a large hydrocarbon fueled vehicle, most likely a launch vehicle powered only by rocket motors burning liquid oxygen and kerosene. Two Russian Soyuz rockets could conceivably have produced the plume. The first was launched from the Baikonur Cosmodrome, Kazakhstan on April 6th; the second was launched from Plesetsk, Russia on April 9. Air parcel trajectory calculations and long-lived tracer gas concentrations in the cloud indicate that the Baikonur rocket launch is the most probable source of the plume. The parcel trajectory calculations do not unambiguously trace the transport of the Soyuz plume from Asia to North America, illustrating serious flaws in the point-to-point trajectory calculations. This chance encounter represents the only measurement of the stratospheric effects of emissions from a rocket powered exclusively with hydrocarbon fuel.
From the A-train satellite mission, more than 11 years of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat satellite measurements are available from 2007 to 2017. In this study, we examine cloud macrophysical changes from a passive sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and two active sensors, CALIPSO and CloudSat (CALCS). MODIS and CALCS capture common features of the cloud changes related to El Niño–Southern Oscillation (ENSO) events, i.e., increase of low clouds during La Niña and increase of mid and high clouds during El Niño over the eastern Pacific. However, optically thin cirrus clouds are well detected by CALCS while these are often missed by MODIS. As a result, MODIS shows much flatter distributions of cloud top heights. In addition, compared to MODIS, CALCS cloud volume anomalies are better correlated with relative humidity anomalies. The differences between MODIS and CALCS appear in low cloud variations. Particularly, fluctuations in MODIS low cloud anomalies are larger than CALCS, and MODIS low cloud anomalies are anticorrelated with mid/high cloud anomalies. This is because of the limitation in detecting underlying clouds by MODIS passive sensor. Also, the layer thickness of MODIS mid/high clouds is thinner than that from CALCS, less affecting cloud amounts at 0-3 km altitude.
This document provides five brief reports that address several algorithm investigations sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. This volume, therefore, has been designated as the first in a series of algorithm volumes. Chapter 1 describes the initial suite of masks, used to prevent further processing of contaminated radiometric data, and flags, which are employed to mark data whose quality (due to a variety of factors) may be suspect. In addition to providing the mask and flag algorithms, this chapter also describes the initial strategy for their implementation. Chapter 2 evaluates various strategies for the detection of clouds and ice in high latitude (polar and sub-polar regions) using Coastal Zone Color Scanner (CZCS) data. Chapter 3 presents an algorithm designed for detecting and masking coccolithosphore blooms in the open ocean. Chapter 4 outlines a proposed scheme for correcting the out-of-band response when SeaWiFS is in orbit. Chapter 5 gives a detailed description of the algorithm designed to apply sensor calibration data during the processing of level-1b data.
We report the first detection of molecular oxygen in interstellar clouds by observation.
Remote sensing of cloud and aerosol optical properties is routinely obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites. Instruments that are being used to enhance our ability to characterize the global distribution of cloud and aerosol properties include well-calibrated multispectral radiometers that measure in the visible, near-infrared, and thermal infrared. The availability of thermal channels to enhance detection of cloud when estimating aerosol properties is an important improvement. In this paper, we describe the radiative properties of clouds as currently determined from satellites (cloud fraction, optical thickness, cloud top pressure, and cloud particle effective radius) and highlight the global/regional cloud microphysical properties currently available for assessing climate variability and forcing. These include the latitudinal distribution of cloud optical and radiative properties of both liquid water and ice clouds, as well as joint histograms of cloud optical thickness and effective particle radius for selected geographical locations around the world. In addition, we will illustrate the radiative and microphysical properties of aerosol particles (in cloud free regions) that are currently available from space-based observations, and show the latitudinal distribution of aerosol optical properties over both land and ocean surfaces.
The Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) is designed for mapping of the atmospheric ozone distribution. Absorption by sulfur dioxide at the same ultraviolet spectral wavelengths makes it possible to observe and resolve the size of volcanic clouds. The sulfur dioxide absorption is discriminated from ozone and water clouds in the data processing by their spectral signatures. Thus, the sulfur dioxide can serve as a tracer which appears in volcanic eruption clouds because it is not present in other clouds. The detection limit with TOMS is close to the theoretical limit due to telemetry signal quantization of 1000 metric tons (5-sigma threshold) within the instrument field of view (50 by 50 km near the nadir). Requirements concerning the use of TOMS in detection of eruptions, geochemical cycles, and volcanic climatic effects are discussed.
Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.
This study presents an approach that converts the vertical profiles of grid-averaged cloud properties from large-scale models to probability density functions (pdfs) of subgrid-cell cloud physical properties measured at satellite footprints. Cloud physical and radiative properties, rather than just cloud and precipitation occurrences, of assimilated cloud systems by the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis (EOA) and ECMWF Re-Analyses (ERA-40 and ERA Interim) are validated against those obtained from Earth Observing System satellite cloud object data for January-August 1998 and March 2000 periods. These properties include ice water path (IWP), cloud-top height and temperature, cloud optical depth and solar and infrared radiative fluxes. Each cloud object, a contiguous region with similar cloud physical properties, is temporally and spatially matched with EOA and ERA-40 data. Results indicate that most pdfs of EOA and ERA-40 cloud physical and radiative properties agree with those of satellite observations of the tropical deep convective cloud-object type for the January-August 1998 period. There are, however, significant discrepancies in selected ranges of the cloud property pdfs such as the upper range of EOA cloud top height. A major discrepancy is that the dependence of the pdfs on the cloud object size for both EOA and ERA-40 is not as strong as in the observations. Modifications to the cloud parameterization in ECMWF that occurred in October 1999 eliminate the clouds near the tropopause but shift power of the pdf to lower cloud-top heights and greatly reduce the ranges of IWP and cloud optical depth pdfs. These features persist in ERA-40 due to the use of the same cloud parameterizations. The downgrade of data assimilation technique and the lack of snow water content information in ERA-40, not the coarser horizontal grid resolution, are also responsible for the disagreements with observed pdfs of cloud physical properties although the detection rates of cloud object occurrence are improved for small size categories. A possible improvement to the convective parameterization is to introduce a stronger dependence of updraft penetration heights with grid-cell dynamics. These conclusions will be rechecked using the ERA Interim data, due to recent changes in the ECMWF convective parameterization (Bechtold et al. 2004, 2008). Results from the ERA Interim will be presented at the meeting.
Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.
Using proxy data for the occurrence of those mass ejections from the solar corona which are directed earthward, we investigate the association between the post-1970 interplanetary magnetic clouds of Klein and Burlaga and coronal mass ejections. The evidence linking magnetic clouds following shocks with coronal mass ejections is striking; six of nine clouds observed at Earth were preceded an appropriate time earlier by meter-wave type II radio bursts indicative of coronal shock waves and coronal mass ejections occurring near central meridian. During the selected periods when no clouds were detected near Earth, the only type II bursts reported were associated with solar activity near the limbs. Where the proxy solar data to be sought are not so clearly suggested, that is, for clouds preceding interaction regions and clouds within cold magnetic enhancements, the evidence linking the clouds and coronal mass ejections is not as clear; proxy data usually suggest many candidate mass-ejection events for each cloud. Overall, the data are consistent with and support the hypothesis suggested by Klein and Burlaga that magnetic clouds observed with spacecraft at 1 AU are manifestations of solar coronal mass ejection transients.
Using proxy data for the occurrence of those mass ejections from the solar corona which are directed earthward, the association between the post-1970 interplanetary magnetic clouds of Klein and Burlaga (1982) and coronal mass ejections is investigated. The evidence linking magnetic clouds following shocks with coronal mass ejections is striking; six of nine clouds observed at earth were preceded an appropriate time earlier by meter-wave type II radio bursts indicative of coronal shock waves and coronal mass ejections occurring near central meridian. During the selected control periods when no clouds were detected near earth, the only type II bursts reported were associated with solar activity near the limbs. Where the proxy solar data to be sought are not so clearly suggested, that is, for clouds preceding interaction regions and clouds within cold magnetic enhancements, the evidence linking the clouds and coronal mass ejections is not as clear; proxy data usually suggest many candidate mass-ejection events for each cloud.